Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must fully disclose behavior. It mentions data sources, adjustable time window, and types of returns. However, it omits details on rate limits, authentication, data freshness, or error handling, which are important for agentic usage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences long, front-loads the core purpose and outputs, and uses no superfluous language. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite lacking an output schema, the description enumerates return data (total mentions, buckets, velocity, sentiment, top posts). It covers data sources, time window, and use cases. Could mention pagination or result limits, but overall complete for a simple tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents all three parameters. The description adds marginal value by noting the default and max for 'days' and providing examples for 'term'. Baseline 3 is appropriate as the description does not significantly augment the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: counting mentions, velocity, and sentiment for a term across Reddit and Hacker News. It lists specific outputs and provides example use cases, effectively distinguishing it from sibling tools like emerging_terms and trending_topics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly recommends use cases such as brand-watch, ticker-buzz, product-launch tracking, and trend-confirmation. It implies when to use it but does not explicitly state when not to or provide direct alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.